Twitter, Facebook, Lyft layoffs spark fears of dotcom crash 2.0

A crypto-collapse, layoffs at Facebook and carnage at Twitter are rocking the tech industry. It’s stoking memories of the dot-com crash 20 years ago.

LAYOFFVIZ (Laura Padilla Castellanos/The Washington Post)


Over the past week, Silicon Valley companies have laid off 20,000 employees, a swift ramp-up of the job cuts and hiring freezes that have been ricocheting through the tech industry for months.

Twitter, Facebook parent Meta, payment platform Stripe, software service firm Salesforce, ride-hailing company Lyft and a growing list of smaller companies all laid off double-digit percentages of their workers. That means tens of thousands of engineers, salespeople and support staff in one of the country’s most important and highest-paying industries are out of a job. Meanwhile, other companies including Google and Amazon have recently instated hiring slowdowns and freezes.

The departures are solidifying a feeling in Silicon Valley that the bull market of the past decade — which created massive amounts of wealth for tech investors, workers and the broader economy — is decidedly over, conjuring an image of what the rest of the economy could experience if a predicted recession materializes.

“It does feel a little like 2000,” said Lise Buyer, a longtime tech analyst, executive and investor, referring to the turn-of-the-century dot-com crash. “Hire engineers, hire engineers, hire engineers, and then suddenly companies get a cold bucket of water in their face.”

Corporations on

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Spark on Hadoop integration with Jupyter

For several years, Jupyter notebook has established itself as the notebook solution in the Python universe. Historically, Jupyter is the tool of choice for data scientists who mainly develop in Python. Over the years, Jupyter has evolved and now has a wide range of features thanks to its plugins. Also, one of the main advantages of Jupyter is its ease of deployment.

More and more Spark developers favor Python over Scala to develop their various jobs for speed of development.

In this article, we will see together how to connect a Jupyter server to a Spark cluster running on Hadoop Yarn secured with Kerberos.

How to install Jupyter?

We cover two methods to connect Jupyter to a Spark cluster:

  1. Set up a script to launch a Jupyter instance that will have a Python Spark interpreter.
  2. Connect Jupyter notebook to a Spark cluster via the Sparkmagic extension.

Method 1: Create a startup script


  • Have access to a Spark cluster machine, usually a master node or a edge node;
  • Having an environment (Conda, Mamba, virtualenv, ..) with the ‘jupyter’ package. Example with Conda: conda create -n pysparktest python=3.7 jupyter.

Create a script in /home directory and insert the following code by modifying the paths so that they correspond to your environment:

#! /bin/bash

export PYSPARK_PYTHON=/home/adaltas/.conda/envs/pysparktest/bin/python

export PYSPARK_DRIVER_PYTHON=/home/adaltas/.conda/envs/pysparktest/bin/ipython3

export PYSPARK_DRIVER_PYTHON_OPTS="notebook --no-browser --ip="

pyspark \
  --master yarn \
  --conf spark.shuffle.service.enabled=true \
  --conf spark.dynamicAllocation.enabled=false \
  --driver-cores 2 --driver-memory 11136m 
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